In image analysis, feature detection refers to computing abstractions of an image and determining whether points are indicative of various kinds of attributes, such as edges, blobs, ridges, etc. While feature detection may be utilized to recognize attributes that may be indicative of rudimentary features, such as attributes that are indicative of a face in an image, such analysis is not well adapted to identifying features in crowded images, or features that are difficult to discern even by a human observer. In some cases, as with medical imaging, medical expertise and experience may be necessary before features such as a bone fracture can be identified in an x-ray by a human observer. The complexity and or subtlety of detecting features in such cases may inhibit the performance of automatic feature detection. In these cases, steps that are customarily a part of image analysis, such as applying Gaussian filters, etc. may reduce the accuracy of the analysis, or greatly increase computational complexity.